Identifyingpotential drug-target interactions (DTIs) is crucial for understanding drug mechanisms, and recent computational methods have yielded promising results in this area. However, these methods face several challenges, including limited model generalization due to heavy reliance on multiple similarity datasets and complex feature extraction, as well as a lack of interpretability by ignoring intrinsic information about drugs and targets. To address these challenges, we propose DrugKANs, a novel DTI prediction model that enhances both the quality and interpretability of DTI representations by integrating a dual-tower architecture with Kolmogorov-Arnold Network (KAN) technology. Our model involves utilizing a pre-trained model to derive initial representations of drugs and targets, and employing a lightweight attention mechanism to capture key features, thereby improving representation quality. We leverage the dual-tower architecture and a lightweight feature interaction mechanism to extract high-level representations separately for drugs and targets, aiming to reduce complex feature interactions and mitigate overfitting. Additionally, we incorporate a contrastive learning strategy within the drug-target bipartite graph to address sparse neighborhood effects and enhance topological information. The inclusion of KAN technology further improves the interpretability of the DTI prediction model. Experimental results on public datasets demonstrate that our model predicts DTIs effectively, underscoring its potential as a valuable tool in drug discovery. This comprehensive methodology presents a balanced approach to overcoming the identified challenges in DTI prediction.

T-cell receptor (TCR)-epitope binding prediction is critical for immunotherapies but remains challenged by sparse interaction networks and severe class imbalance in training data. Current graph neural network (GNN) approaches for predicting TCR-epitope binding (TEB) fail to address two key limitations: over-smoothing during message propagation in sparse TCR-epitope graphs and biased predictions toward dominant epitope-TCR pairs. Here, we present GRAPE (Graph-Regularized Attentive Protein Embeddings), a framework unifying spectral graph regularization and imbalance-aware learning. GRAPE first leverages protein language models (ESM-2) to generate evolutionary-informed TCR/epitope embeddings, constructing a topology-aware interaction graph. To mitigate over-smoothing, we introduce spectral graph regularization, explicitly constraining node feature smoothness to preserve discriminative patterns in sparse neighborhoods. Simultaneously, a dynamic edge reweighting module prioritizes unobserved TCR-epitope edges during graph propagation, coupled with a differentiable area under the ROC curve-maximization objective that directly optimizes for imbalance resilience. Extensive benchmarking on public datasets demonstrates that GRAPE significantly outperforms state-of-the-art methods in TEB prediction. This work establishes GRAPE as a robust framework for elucidating TCR-epitope interactions, with broad applications in immunology research and therapeutic design.

Drug discovery and development constitute a laborious and costly undertaking. The success of a drug hinges not only good efficacy but also acceptable absorption, distribution, metabolism, elimination, and toxicity (ADMET) properties. Overall, up to 50% of drug development failures have been contributed from undesirable ADMET profiles. As a multiple parameter objective, the optimization of the ADMET properties is extremely challenging owing to the vast chemical space and limited human expert knowledge. In this study, a freely available platform called Chemical Molecular Optimization, Representation and Translation (ChemMORT) is developed for the optimization of multiple ADMET endpoints without the loss of potency (https://cadd.nscc-tj.cn/deploy/chemmort/). ChemMORT contains three modules: Simplified Molecular Input Line Entry System (SMILES) Encoder, Descriptor Decoder and Molecular Optimizer. The SMILES Encoder can generate the molecular representation with a 512-dimensional vector, and the Descriptor Decoder is able to translate the above representation to the corresponding molecular structure with high accuracy. Based on reversible molecular representation and particle swarm optimization strategy, the Molecular Optimizer can be used to effectively optimize undesirable ADMET properties without the loss of bioactivity, which essentially accomplishes the design of inverse QSAR. The constrained multi-objective optimization of the poly (ADP-ribose) polymerase-1 inhibitor is provided as the case to explore the utility of ChemMORT.

Machine learning-based scoring functions (MLSFs) have become a very favorable alternative to classical scoring functions because of their potential superior screening performance. However, the information of negative data used to construct MLSFs was rarely reported in the literature, and meanwhile the putative inactive molecules recorded in existing databases usually have obvious bias from active molecules. Here we proposed an easy-to-use method named AMLSF that combines active learning using negative molecular selection strategies with MLSF, which can iteratively improve the quality of inactive sets and thus reduce the false positive rate of virtual screening. We chose energy auxiliary terms learning as the MLSF and validated our method on eight targets in the diverse subset of DUD-E. For each target, we screened the IterBioScreen database by AMLSF and compared the screening results with those of the four control models. The results illustrate that the number of active molecules in the top 1000 molecules identified by AMLSF was significantly higher than those identified by the control models. In addition, the free energy calculation results for the top 10 molecules screened out by the AMLSF, null model and control models based on DUD-E also proved that more active molecules can be identified, and the false positive rate can be reduced by AMLSF.

BACKGROUND: Allele-specific binding (ASB) events occur when transcription factors (TFs) bind more favorably to one of the two parental alleles at heterozygous single nucleotide polymorphisms (SNPs). Evidence suggests that ASB events could reveal the impact of sequence variations on TF binding and may have implications for the risk of diseases. RESULTS: Here we present ASB-analyzer, a software platform that enables the users to quickly and efficiently input raw sequencing data to generate individual reports containing the cytogenetic map of ASB SNPs and their associated phenotypes. This interactive tool thereby combines ASB SNP identification, biological annotation, motif analysis, phenotype associations and report summary in one pipeline. With this pipeline, we identified 3772 ASB SNPs from thirty GM12878 ChIP-seq datasets and demonstrated that the ASB SNPs were more likely to be enriched at important sites in TF-binding domains. CONCLUSIONS: ASB-analyzer is a user-friendly tool that enables the detection, characterization and visualization of ASB SNPs. It is implemented in Python, R and bash shell and packaged in the Conda environment. It is available as an open-source tool on GitHub at https://github.com/Liying1996/ASBanalyzer .

BACKGROUND: With the increasing development of biotechnology and information technology, publicly available data in chemistry and biology are undergoing explosive growth. Such wealthy information in these resources needs to be extracted and then transformed to useful knowledge by various data mining methods. However, a main computational challenge is how to effectively represent or encode molecular objects under investigation such as chemicals, proteins, DNAs and even complicated interactions when data mining methods are employed. To further explore these complicated data, an integrated toolkit to represent different types of molecular objects and support various data mining algorithms is urgently needed. RESULTS: We developed a freely available R/CRAN package, called BioMedR, for molecular representations of chemicals, proteins, DNAs and pairwise samples of their interactions. The current version of BioMedR could calculate 293 molecular descriptors and 13 kinds of molecular fingerprints for small molecules, 9920 protein descriptors based on protein sequences and six types of generalized scale-based descriptors for proteochemometric modeling, more than 6000 DNA descriptors from nucleotide sequences and six types of interaction descriptors using three different combining strategies. Moreover, this package realized five similarity calculation methods and four powerful clustering algorithms as well as several useful auxiliary tools, which aims at building an integrated analysis pipeline for data acquisition, data checking, descriptor calculation and data modeling. CONCLUSION: BioMedR provides a comprehensive and uniform R package to link up different representations of molecular objects with each other and will benefit cheminformatics/bioinformatics and other biomedical users. It is available at: https://CRAN.R-project.org/package=BioMedR and https://github.com/wind22zhu/BioMedR/.

Matched molecular pairs analysis (MMPA) has become a powerful tool for automatically and systematically identifying medicinal chemistry transformations from compound/property datasets. However, accurate determination of matched molecular pair (MMP) transformations largely depend on the size and quality of existing experimental data. Lack of high-quality experimental data heavily hampers the extraction of more effective medicinal chemistry knowledge. Here, we developed a new strategy called quantitative structure-activity relationship (QSAR)-assisted-MMPA to expand the number of chemical transformations and took the logD7.4 property endpoint as an example to demonstrate the reliability of the new method. A reliable logD7.4 consensus prediction model was firstly established, and its applicability domain was strictly assessed. By applying the reliable logD7.4 prediction model to screen two chemical databases, we obtained more high-quality logD7.4 data by defining a strict applicability domain threshold. Then, MMPA was performed on the predicted data and experimental data to derive more chemical rules. To validate the reliability of the chemical rules, we compared the magnitude and directionality of the property changes of the predicted rules with those of the measured rules. Then, we compared the novel chemical rules generated by our proposed approach with the published chemical rules, and found that the magnitude and directionality of the property changes were consistent, indicating that the proposed QSAR-assisted-MMPA approach has the potential to enrich the collection of rule types or even identify completely novel rules. Finally, we found that the number of the MMP rules derived from the experimental data could be amplified by the predicted data, which is helpful for us to analyze the medicinal chemical rules in local chemical environment. In summary, the proposed QSAR-assisted-MMPA approach could be regarded as a very promising strategy to expand the chemical transformation space for lead optimization, especially when no enough experimental data can support MMPA.

Computational methods have become indispensable tools to accelerate the drug discovery process and alleviate the excessive dependence on time-consuming and labor-intensive experiments. Traditional feature-engineering approaches heavily rely on expert knowledge to devise useful features, which could be costly and sometimes biased. The emerging deep learning (DL) methods deliver a data-driven method to automatically learn expressive representations from complex raw data. Inspired by this, researchers have attempted to apply various deep neural network models to simplified molecular input line entry specification (SMILES) strings, which contain all the composition and structure information of molecules. However, current models usually suffer from the scarcity of labeled data. This results in a low generalization ability of SMILES-based DL models, which prevents them from competing with the state-of-the-art computational methods. In this study, we utilized the BiLSTM (bidirectional long short term merory) attention network (BAN) in which we employed a novel multi-step attention mechanism to facilitate the extracting of key features from the SMILES strings. Meanwhile, SMILES enumeration was utilized as a data augmentation method in the training phase to substantially increase the number of labeled data and enlarge the probability of mining more patterns from complex SMILES. We again took advantage of SMILES enumeration in the prediction phase to rectify model prediction bias and provide a more accurate prediction. Combined with the BAN model, our strategies can greatly improve the performance of latent features learned from SMILES strings. In 11 canonical absorption, distribution, metabolism, excretion and toxicity-related tasks, our method outperformed the state-of-the-art approaches.

Scoring functions (SFs) based on complex machine learning (ML) algorithms have gradually emerged as a promising alternative to overcome the weaknesses of classical SFs. However, extensive efforts have been devoted to the development of SFs based on new protein-ligand interaction representations and advanced alternative ML algorithms instead of the energy components obtained by the decomposition of existing SFs. Here, we propose a new method named energy auxiliary terms learning (EATL), in which the scoring components are extracted and used as the input for the development of three levels of ML SFs including EATL SFs, docking-EATL SFs and comprehensive SFs with ascending VS performance. The EATL approach not only outperforms classical SFs for the absolute performance (ROC) and initial enrichment (BEDROC) but also yields comparable performance compared with other advanced ML-based methods on the diverse subset of Directory of Useful Decoys: Enhanced (DUD-E). The test on the relatively unbiased actives as decoys (AD) dataset also proved the effectiveness of EATL. Furthermore, the idea of learning from SF components to yield improved screening power can also be extended to other docking programs and SFs available.

BACKGROUND: Fluorescent detection methods are indispensable tools for chemical biology. However, the frequent appearance of potential fluorescent compound has greatly interfered with the recognition of compounds with genuine activity. Such fluorescence interference is especially difficult to identify as it is reproducible and possesses concentration-dependent characteristic. Therefore, the development of a credible screening tool to detect fluorescent compounds from chemical libraries is urgently needed in early stages of drug discovery. RESULTS: In this study, we developed a webserver ChemFLuo for fluorescent compound detection, based on two large and high-quality training datasets containing 4906 blue and 8632 green fluorescent compounds. These molecules were used to construct a group of prediction models based on the combination of three machine learning algorithms and seven types of molecular representations. The best blue fluorescence prediction model achieved with balanced accuracy (BA) = 0.858 and area under the receiver operating characteristic curve (AUC) = 0.931 for the validation set, and BA = 0.823 and AUC = 0.903 for the test set. The best green fluorescence prediction model achieved the prediction accuracy with BA = 0.810 and AUC = 0.887 for the validation set, and BA = 0.771 and AUC = 0.852 for the test set. Besides prediction model, 22 blue and 16 green representative fluorescent substructures were summarized for the screening of potential fluorescent compounds. The comparison with other fluorescence detection tools and theapplication to external validation sets and large molecule libraries have demonstrated the reliability of prediction model for fluorescent compound detection. CONCLUSION: ChemFLuo is a public webserver to filter out compounds with undesirable fluorescent properties, which will benefit the design of high-quality chemical libraries for drug discovery. It is freely available at http://admet.scbdd.com/chemfluo/index/.

BACKGROUND: High-throughput screening (HTS) and virtual screening (VS) have been widely used to identify potential hits from large chemical libraries. However, the frequent occurrence of 'noisy compounds' in the screened libraries, such as compounds with poor drug-likeness, poor selectivity or potential toxicity, has greatly weakened the enrichment capability of HTS and VS campaigns. Therefore, the development of comprehensive and credible tools to detect noisy compounds from chemical libraries is urgently needed in early stages of drug discovery. RESULTS: In this study, we developed a freely available integrated python library for negative design, called Scopy, which supports the functions of data preparation, calculation of descriptors, scaffolds and screening filters, and data visualization. The current version of Scopy can calculate 39 basic molecular properties, 3 comprehensive molecular evaluation scores, 2 types of molecular scaffolds, 6 types of substructure descriptors and 2 types of fingerprints. A number of important screening rules are also provided by Scopy, including 15 drug-likeness rules (13 drug-likeness rules and 2 building block rules), 8 frequent hitter rules (four assay interference substructure filters and four promiscuous compound substructure filters), and 11 toxicophore filters (five human-related toxicity substructure filters, three environment-related toxicity substructure filters and three comprehensive toxicity substructure filters). Moreover, this library supports four different visualization functions to help users to gain a better understanding of the screened data, including basic feature radar chart, feature-feature-related scatter diagram, functional group marker gram and cloud gram. CONCLUSION: Scopy provides a comprehensive Python package to filter out compounds with undesirable properties or substructures, which will benefit the design of high-quality chemical libraries for drug design and discovery. It is freely available at https://github.com/kotori-y/Scopy.

BACKGROUND: Substructure screening is widely applied to evaluate the molecular potency and ADMET properties of compounds in drug discovery pipelines, and it can also be used to interpret QSAR models for the design of new compounds with desirable physicochemical and biological properties. With the continuous accumulation of more experimental data, data-driven computational systems which can derive representative substructures from large chemical libraries attract more attention. Therefore, the development of an integrated and convenient tool to generate and implement representative substructures is urgently needed. RESULTS: In this study, PySmash, a user-friendly and powerful tool to generate different types of representative substructures, was developed. The current version of PySmash provides both a Python package and an individual executable program, which achieves ease of operation and pipeline integration. Three types of substructure generation algorithms, including circular, path-based and functional group-based algorithms, are provided. Users can conveniently customize their own requirements for substructure size, accuracy and coverage, statistical significance and parallel computation during execution. Besides, PySmash provides the function for external data screening. CONCLUSION: PySmash, a user-friendly and integrated tool for the automatic generation and implementation of representative substructures, is presented. Three screening examples, including toxicophore derivation, privileged motif detection and the integration of substructures with machine learning (ML) models, are provided to illustrate the utility of PySmash in safety profile evaluation, therapeutic activity exploration and molecular optimization, respectively. Its executable program and Python package are available at https://github.com/kotori-y/pySmash.

The poly (ADP-ribose) polymerase-1 (PARP1) has been regarded as a vital target in recent years and PARP1 inhibitors can be used for ovarian and breast cancer therapies. However, it has been realized that most of PARP1 inhibitors have disadvantages of low solubility and permeability. Therefore, by discovering more molecules with novel frameworks, it would have greater opportunities to apply it into broader clinical fields and have a more profound significance. In the present study, multiple virtual screening (VS) methods had been employed to evaluate the screening efficiency of ligand-based, structure-based and data fusion methods on PARP1 target. The VS methods include 2D similarity screening, structure-activity relationship (SAR) models, docking and complex-based pharmacophore screening. Moreover, the sum rank, sum score and reciprocal rank were also adopted for data fusion methods. The evaluation results show that the similarity searching based on Torsion fingerprint, six SAR models, Glide docking and pharmacophore screening using Phase have excellent screening performance. The best data fusion method is the reciprocal rank, but the sum score also performs well in framework enrichment. In general, the ligand-based VS methods show better performance on PARP1 inhibitor screening. These findings confirmed that adding ligand-based methods to the early screening stage will greatly improve the screening efficiency, and be able to enrich more highly active PARP1 inhibitors with diverse structures.

BACKGROUND: Cartilage damage is a crucial feature involved in several pathological conditions characterized by joint disorders, such as osteoarthritis and rheumatoid arthritis. Accumulated evidences showed that Wnt/β-catenin pathway plays a role in the pathogenesis of cartilage damage. In addition, it is experimentally documented that lncRNA (long non-coding RNA) HOTAIR plays a key role in the regulation of Wnt/β-catenin pathway based on directly decreased WIF-1 expression. Further, it is reported that Wnt/β-catenin pathway is a potent pathway to regulate the expression of MMP-13, which is responsible for degradation of collagen type II in articular cartilage. It is increasingly recognized that systems modeling approach provides an opportunity to understand the complex relationships and direct quantitative analysis of dynamic network in various diseases. RESULTS: A dynamic network of lncRNA HOTAIR-mediated Wnt/β-catenin pathway regulating MMP-13 is developed to investigate the dynamic mechanism of the network involved in the pathogenesis of cartilage damage. Based on the network modeling, the potential therapeutic intervention point Axin is predicted and confirmed by the experimental validation. CONCLUSIONS: Our study provides a promising strategy for revealing potential dynamic mechanism and assessing potential targets which contribute to the prevention of the pathological conditions related to cartilage damage.

BACKGROUND: Many computational approaches have been used for target prediction, including machine learning, reverse docking, bioactivity spectra analysis, and chemical similarity searching. Recent studies have suggested that chemical similarity searching may be driven by the most-similar ligand. However, the extent of bioactivity of most-similar ligands has been oversimplified or even neglected in these studies, and this has impaired the prediction power. RESULTS: Here we propose the MOst-Similar ligand-based Target inference approach, namely MOST, which uses fingerprint similarity and explicit bioactivity of the most-similar ligands to predict targets of the query compound. Performance of MOST was evaluated by using combinations of different fingerprint schemes, machine learning methods, and bioactivity representations. In sevenfold cross-validation with a benchmark Ki dataset from CHEMBL release 19 containing 61,937 bioactivity data of 173 human targets, MOST achieved high average prediction accuracy (0.95 for pKi ≥ 5, and 0.87 for pKi ≥ 6). Morgan fingerprint was shown to be slightly better than FP2. Logistic Regression and Random Forest methods performed better than Naïve Bayes. In a temporal validation, the Ki dataset from CHEMBL19 were used to train models and predict the bioactivity of newly deposited ligands in CHEMBL20. MOST also performed well with high accuracy (0.90 for pKi ≥ 5, and 0.76 for pKi ≥ 6), when Logistic Regression and Morgan fingerprint were employed. Furthermore, the p values associated with explicit bioactivity were found be a robust index for removing false positive predictions. Implicit bioactivity did not offer this capability. Finally, p values generated with Logistic Regression, Morgan fingerprint and explicit activity were integrated with a false discovery rate (FDR) control procedure to reduce false positives in multiple-target prediction scenario, and the success of this strategy it was demonstrated with a case of fluanisone. In the case of aloe-emodin's laxative effect, MOST predicted that acetylcholinesterase was the mechanism-of-action target; in vivo studies validated this prediction. CONCLUSIONS: Using the MOST approach can result in highly accurate and robust target prediction. Integrated with a FDR control procedure, MOST provides a reliable framework for multiple-target inference. It has prospective applications in drug repurposing and mechanism-of-action target prediction.

MOTIVATION: Drug combinations are a promising strategy for combating complex diseases by improving the efficacy and reducing corresponding side effects. Currently, a widely studied problem in pharmacology is to predict effective drug combinations, either through empirically screening in clinic or pure experimental trials. However, the large-scale prediction of drug combination by a systems method is rarely considered. RESULTS: We report a systems pharmacology framework to predict drug combinations (PreDCs) on a computational model, termed probability ensemble approach (PEA), for analysis of both the efficacy and adverse effects of drug combinations. First, a Bayesian network integrating with a similarity algorithm is developed to model the combinations from drug molecular and pharmacological phenotypes, and the predictions are then assessed with both clinical efficacy and adverse effects. It is illustrated that PEA can predict the combination efficacy of drugs spanning different therapeutic classes with high specificity and sensitivity (AUC = 0.90), which was further validated by independent data or new experimental assays. PEA also evaluates the adverse effects (AUC = 0.95) quantitatively and detects the therapeutic indications for drug combinations. Finally, the PreDC database includes 1571 known and 3269 predicted optimal combinations as well as their potential side effects and therapeutic indications. AVAILABILITY AND IMPLEMENTATION: The PreDC database is available at http://sm.nwsuaf.edu.cn/lsp/predc.php.

It is a long histroy that use of acupuncture treatment coronary heart disease (CHD) which belongs to Xiong Bi in Traditional Chinese Medicine (TCM). In this paper, application of acupuncture on CHD treatment was analyzed by text mining. The study was focused on the fields of meridian, acupoints, acupuncture method and constructed the associated network of them. By analyzing the network, we found that Pericardium Meridian of Hand-Jueyin was the frequently used meridian combined with other meridians and Neiguan was the frequently used acupoint combined with other acupoints for acupuncture treatment on CHD. Electroacupuncture was the common used method and often applied in the meridian of Hand-Jueyin and Hand-Shaoyin. The combinations of meridians, acupoints and methods were demonstrated in network. Results could be helpful for both clinical practice and medical research.

Hypertension becomes a major public health problem due to its serious morbidity and mortality and serious cardiovascular, cerebrovascular and kidney complications. The complementary therapies of Traditional Chinese Medicine (TCM) play important roles in hypertension treatment. Acupuncture, as an important component in TCM, is frequently used in treatment hypertension. In this paper, we mined the data set of literature downloaded from SinoMed on hypertension focused on its treatment with acupuncture technology in TCM common use. The text mining results showed that Shuigou, Zhongshu, Burong, Zusanli, and Taichong were the top 5 acupoints, while Governor Meridian, Bladder Meridian of Foot-Taiyang and Stomach Meridian of Foot-Yangming were the top 3 meridian used for hypertension treatment. The combinations of acupoints were demonstrated in network, which were widely used in the clinical practices of acupuncture. The results provided good references for both clinical practice and medical research.

Shuang-Huang-Lian (SHL) is a famous modern formula prepared from three medicinal herbs including Flos Lonicerae, Radix Scutellariae and Fructus Forsythiae. Currently, SHL has been developed a variety of dosage forms due to its proved clinical efficacy. However, the in-depth research on targets and pharmacological mechanisms of SHL preparations was scarce. In the presented study, the bioinformatics approaches were adopted to integrate relevant data and biological information. As a result, a PPI network was built and the common topological parameters were characterized. The reuslts suggested that the PPI network of SHL exhibited a scale-free property and modular architecture. The drug target network of SHL was structured with 21 functional clusters as modules naturally. According to certain modules and pharmacological effects distribution, an anti-tumor effect and protented durg targets were predicted. In conclutions, a bioinformatics approach was established for exploring the drug targets and pharmacological activity distribution. The results offered a novel anti-tumor effect of SHL and provide a clue for further research and development.

To explore the regularity of clinical medication of Gastro-esophageal reflux disease (GERD) through text mining approach. Literatures on GERD in SinoMed(Chinese biomedical literature service system) were collected. Results: BANXIA, CHAIHU and HUANGLIAN were commonly used Chinese herbs in sequence from high to low, the network of Chinese herbs, symptoms and patterns could be established. Conclusion: The commonly used Chinese Herbs are coincided with the treatment rules of regulating the function of liver and stomach as well as adjusting cold and heat and the networks are corresponded with each other in treating GERD and could be verified through text mining approach.

Acupoint application therapy is one of the Traditional Chinese medicine (TCM) external therapies, which has been used for coronary heart disease (CHD) treatment for a long time. The prescription of acupoint application therapy includes both Chinese herbal medicines (CHMs) and acupoints. This study aims to reveal the basic rules of acupoint selection in acupoint application therapy on CHD treatment by text mining study. Results showed that on CHD treatment, the most frequently used acupoint was Danzhong (RN17), followed by Xinshu (BL15), Neiguan (PC6), Zusanli (ST36) and Zhiyang (DU9). Acupoints closing to the heart or with specific functions were more frequently used. Moreover, Xinshu, Danzhong and Neiguan were of the top 3 relationships with other acupoints, while Zusanli and Sanyinjiao (SP6) were in the lower grade. The combination of Xinshu-Danzhong-Neiguan could be regard as the basic acupoint prescription of acupoint application therapy on CHD.

Tinnitus is the most common medical symptom that can be debilitating. Chinese herbal medicines (CHMs) play important roles in tinnitus treatment. This study analyzed the most frequently prescribed herbs used to treat tinnitus and the molecular mechanisms of therapeutic effects of these CHMs by text mining and bioinformatics technique. Results showed that pinellia ternate (Banxia) and gastrodin (Tianma) are the most frequently prescribed CHMs for treatment tinnitus. The molecular mechanisms of therapeutic effects of two CHMs were focused on sertoli cell-sertoli cell juntion signaling and NRF2-mediated oxidative stress response signaling. Integrative text mining and bioinformatics analysis may be a promising approach to find new drug and decipher their molecular mechanism intuitively.

Mammary gland hyperplasia (MGH) is a kind of endocrine disorder diseases, which is usually characterized by lump and pain in breast and an increased risk event of breast cancer. The complementary therapies of Traditional Chinese Medicine (TCM) play important roles in MGH treatment. This study analyzed the application of the TCM external therapy and food therapy by text mining. Subsequently, suitable techniques and methods which were frequently used on MGH treatment were explored. Results showed that external application of Chinese herb medicine, acupuncture, massage, catgut embedding, and magnet therapy were the top 5 TCM external therapy techniques, while fritillaria Oyster, alga, trionyx, kelp, arrowhead, pseudo-ginseng, lemon, leonurus, and hawthorn were the top 10 foods used for MGH treatment. The combinations of external therapy techniques were demonstrated in network, so does the combinations of the foods. The results provided good references for both clinical practice and basic research.

Fang-Feng-Tang is a Chinese herbal formula which is designed to treat syndrome Feng-Han-Shi-Bi for rheumatoid arthritis (RA). Although its therapeutic effects are widely accepted, its mechanism is still obscure. In this study, by integrating leading knowledge databases of associated different domains, we explored Fang-Feng-Tang's potential therapeutic agents for RA through targeted pathway hierarchies. The molecule mechanism analysis includes compositional herbal medicine, chemical compound, target protein, and pathways enriched. Target proteins are restricted with RA OMIM genes and FDA approved RA drug targets for partial validation. As a result, 8 target proteins were matched and they are UGT1A9, GNT1, UGT1, JAK3, CCR2/5, and CMKBR2/5. What's more, they are mainly involved in pathways of signal transduction, metabolism, immune system, and disease. This research demonstrates that Feng-Feng-Tang's target proteins may regulate a wide range of different systems. This might indicate that Chinese herbal formula's therapeutic effects can be demonstrated through system-level regulation rather than targeting disease specified genes or proteins.

Major depression (MD) is a major public health problem which has a substantial impact on individuals and society. Although it is usually treated with antidepressant in primary care, these therapies accompanied with serious side effects. In the clinic of Chinese medicine, Xiao-Yao-San is the first option for MD with little side effects. Although with clear therapeutic effects, its therapeutic mechanism is obscure. In this study, by integrating leading biomedical knowledge databases, though bioinformatics analysis, we explored the potential therapeutic mechanism of Xiao-Yao-San for MD. The therapeutic mechanism analysis covers aspects of Chinese herbal medicine, chemical compound, target protein, OMIM genes, and pathways enriched. As a result, Xiao-Yao-San's 4 target proteins e.g., ADRB2R, B2AR, ADRB2, and MC4R can be matched in MD's OMIM genes which are mainly enriched in pathways of signal transduction. This research demonstrates that Xiao-Yao-San's target proteins may regulate a wide range of different pathways associated with MD.

Da-Fang-Feng-Tang is a Chinese herbal formula designed to treat syndrome Feng-Han-Shi-Bi for rheumatoid arthritis (RA). Although it's clinical effect have been accepted through 1000 years' clinical practises, its molecule mechanism is still obscure. In this study, by integrating powerful knowledge databases, though bioinformatics analysis, we explored the potential molecular mechanism of Da-Fang-Feng-Tang for RA. The molecule mechanism analysis includes compositional herbal medicine, chemical compound, target protein, RA OMIM genes, and enriched pathways. As a result, Da-Feng-Feng-Tang's 7 target proteins were matched in RA's OMIM genes. What's more, these 7 genes are mainly involved in three pathway hierarchies e.g., disease, immune system, and signal transduction. This research demonstrates that Da-Feng-Feng-Tang's target proteins can regulate a wide range of systems associated with RA. This might indicate that Chinese herbal formula's therapeutic effects are taken effects through system-level regulation rather than targeting only disease specified genes or proteins.

Duhuo-Jisheng-Tang is a Chinese herbal formula designed to treat rheumatoid arthritis (RA) with syndrome dual deficiency of liver and kidney. Although its therapeutic clinical effect is widely accepted over one thousand years, its therapeutic agents are still obscure. In this study, we explored the potential therapeutic agents of Duhuo-Jisheng-Tang for RA by integrating leading knowledge databases of different biomedical domains. The molecule mechanism includes compositional herbal medicine, chemical compound, target protein, RA OMIM genes, FDA approved RA drug targets, and pathways. As a result, Duhuo-Jisheng-Tang's 11 target proteins can be found in RA's OMIM genes (e.g., UGT1, UGT1A9, GNT1, and JAK3) and FDA approved RA drug targets (e.g., CMKBR2/5, CCR2/5, APRF, and STAT1/3). What's more, these 11 genes/proteins are mainly enriched in pathways of disease, immune system, signal transduction, and metabolism. It demonstrates that Duhuo-Jisheng-Tang may regulate a wide range of different systems associated with RA which might indicate that Chinese herbal formula's therapeutic effects are demonstrated through system-level regulation rather than targeting disease specified genes/proteins.

Systems pharmacology is an emerging field that integrates systems biology and pharmacology to advance the process of drug discovery, development and the understanding of therapeutic mechanisms. The aim of the present work is to highlight the role that the systems pharmacology plays across the traditional herbal medicines discipline, which is exemplified by a case study of botanical drugs applied in the treatment of depression. First, based on critically examined pharmacology and clinical knowledge, we propose a large-scale statistical analysis to evaluate the efficiency of herbs used in traditional medicines. Second, we focus on the exploration of the active ingredients and targets by carrying out complex structure-, omics- and network-based systematic investigations. Third, specific informatics methods are developed to infer drug-disease connections, with purpose to understand how drugs work on the specific targets and pathways. Finally, we propose a new systems pharmacology method, which is further applied to an integrated platform (Herbal medicine Systems Pharmacology) of blended herbal medicine and omics data sets, allowing for the systematization of current and traditional knowledge of herbal medicines and, importantly, for the application of this emerging body of knowledge to the development of new drugs for complex human diseases.

Acne is the most common chronic inflammatory skin disease in adolescents and young adults. Many Chinese herbal medicines (CHMs) have been used in the treatment of acne and were proved to be effective and safe. There is growing need to investigate which CHMs are the most frequently prescribed herbs used to treatment acne and what were the molecular mechanisms of therapeutic effects of these CHMs. So the most frequently prescribed CHMs were mined out by a novel text mining method based on a comprehensive collection of 2,136 records of literatures in SinoMed database. The target proteins of these CHMs were retrieved from PubChem database and the genes of acne were searched in Gene database. Ingenuity pathways analysis (IPA) online platform was used for the analysis of the target proteins and the genes. The molecular mechanisms of these CHMs treating acne could be deciphered by building and comparing the related networks and canonical pathways. Results showed that scutellaria baicalensis (Huangqin) and fructus forsythiae (Lianqiao) are the most frequently prescribed CHMs for treatment acne. The network analysis indicated that the associated network functions related with both acne and two CHMs were involved in dermatological diseases, infectious disease and inflammatory disease. The canonical pathway comparison showed that the molecular mechanisms of therapeutic effects of two CHMs were focused on apoptosis signaling and p70s6k signaling. In conclusion, Huangqin and Lianqiao combination might be regarded as a potential new drug for treatment acne and the molecular mechanisms of therapeutic effects could be at least partly due to regulating apoptosis signaling and p70s6k signaling. Integrative text mining and bioinformatics analysis is a promising method to find new drug and decipher their molecular mechanism intuitively.

Much useful information on Ligusticum wallichii (LW) could be obtained from published literature by text mining technique. In this study, the data set on LW was downloaded from Chinese BioMedical literature database (SinoMed). Then, association rules among diseases, traditional Chinese medicine (TCM) syndromes, formulae and herbs on LW were investigated by text mining technique. These rules include TCM syndromes to diseases, formulae and combinational herbs, respectively. Diseases related with formulae including LW were mined out by executing data slicing algorithm. Finally, the results were visually demonstrated with Cytoscape 2.8 software. The main features from the mining data were: (1) LW was frequently used in treating cerebral infraction; (2) Blood stasis due to Qi deficiency was the main syndrome in TCM clinical practice; (3) Angelica sinensis was the first herb to combine with LW according to co-occurrent frequency; (4) Associated with LW, networks of TCM syndromes-diseases, formulae-diseases, TCM syndromes-formulae, and TCM syndromes-combinational herbs were constructed. These associated networks represented a holistic thinking of Chinese medicinal therapy, which might embody association rules among diseases, syndromes, formulae and herbs on LW.

Objectives: In order to better understand the synergetic activities of radix aconite lateralis praeparata (Fuzi)-rheum officinale (Dahuang) herb pairs. Material and methods: The target proteins of Fuzi and Dahuang were retrieved from PubChem database and were uploaded to ingenuity pathway analysis (IPA) platform. Then we carried out molecular network analysis and canonical pathway analysis. Finally, we found out specific molecules, networks and pathways, which embodied the synergetic activities of Fuzi-Dahuang herb pairs. Results: 5 target proteins in Fuzi and 44 target proteins in Dahuang were retrieved from PubChem database. Ubiquitin C (UBC) is a shared molecule in Fuzi network and Dahuang network. Bio-functions of Fuzi and Dahuang involved in gastrointestinal disease, cardiovascular disease, endocrine system disorders, inflammatory disease, as well as renal and urological disease, etc. 14-3-3 mediated signaling is the pathway related with tubulin and tau that are from Fuzi and Dahuang target proteins respectively. Conclusions: Regulating UBC and 14-3-3 mediated signaling pathway might partly decipher the synergetic biological activities of Fuzi-Dahuang herb pairs. Network pharmacology approaches contribute to find the biological activities of herb pairs fully. This study provides a new paradigm for better understanding the synergy mechanism of the compatibility of herb pairs.

In traditional Chinese medicine, rules of Li-Fa-Fang-Yao is of critical importance in clinical practices. Li-Fa-Fang-Yao, which means principles, methods, formulae, and Chinese herbal medicines respectively, indicate the four basic steps of diagnosis and treatment: determining the cause, mechanism and location of the disease according to the medical theories and principles, then deciding the treatment principle and method, and finally selecting a formula as well as proper Chinese herbal medicines. In this paper, focused on major depressive disorder, we explored the rules of Li-Fa-Fang-Yao within the framework of traditional Chinese medicine. Through calculation, three clusters of Li-Fa-Fang-Yao on major depressive disorder were found based on the syndrome differentiation. What's more, these three clusters can also be validated by textbooks of traditional Chinese medicine.

Hypertension becomes a major public health problem due to its high prevalence and serious complications. The complementary therapies of Traditional Chinese Medicine (TCM) play important roles in hypertension treatment. This study analyzed the application of the TCM external therapy and food therapy by text mining. Subsequently, suitable techniques and methods which were frequently used on hypertension treatment were explored. The text mining results showed that acupuncture, massage, Tuina, auricular-plaster and external application of Chinese herb medicine(CHM) were the top 5 TCM external therapy techniques, while placenta, lily root, pseudo-ginseng, peanut, sea horse, wine, radix aconiti carmichaeli, lemon, soybean and fructus crataegi were the top 10 foods used for hypertension treatment. The combinations of external therapy techniques were demonstrated in network, so does the combinations of the foods. The results provided good references for both clinical practice and medical research.

Non-alcoholic fatty liver disease (NAFLD) is a kind of prevalence diseases. Traditional Chinese medicine (TCM) has better efficacy on treating NAFLD. But there are also not known about the critical pathogenesis and the corresponding biological factors. Regarding this, we addressed a text mining approach to analyze the pattern profile, rule of medication, and the pathological factors of NAFLD from the opening database (SinoMed and PubMed). Based on canonical data source, we have our data treatment scheduled in 4 steps: (1) data retrieving, (2) data pretreating, (3) data analyzing, and (4) data visualization. And according to the TCM theory of formulae-pattern-disease' correlation, we partly understand the possible TCM pathogenesis of NAFLD which linked biological process of lipid metabolism disorder, inflammation, and metabolic regulation confusion.

Rheumatoid arthritis (RA) is an autoimmune disease that results in a chronic, systemic inflammatory disorder that may affect many tissues and organs, but principally attacks flexible (synovial) joints. Traditional Chinese medicine (TCM) treats RA with herbal formulae according to different syndrome/pattern classification. One important syndrome/pattern in RA is Feng-Han-Shi-Bi. Targeting this syndrome, Juan-Bi-Tang is the basic herbal formula adopted in clinical practises with reliable therapeutic effect. However, the mechanism of Juan-Bi-Tang is still not clear. In this study, we explored the molecular mechanism of Juan-Bi-Tang with in silico analysis on its 11 herbal medicines' chemical compounds, target proteins, protein-protein interaction, and pathway. As a result, the molecular mechanism of Juan-Bi-Tang for Feng-Han-Shi-Bi syndrome in RA covers a variety of biological processes including immune system, signal transduction, metabolism, disease etc. Most of them are relevant to RA which indicate actual therapeutic effect of TCM clinical practises.

It is a still a world-wide challenge that hypertensive patients maintain a satisfactory blood pressure control. In TCM practice, traditional physical therapy has shown beneficial to blood pressure (BP) controlling. As the amount of bio medical data in leading databases (i.e. SinoMed, etc.) is growing at an exponential rate, it might be possible to get something meaningful through the techniques developed in data mining. In this paper, focused on hypertension, we proposed an algorithm named two dimensions data slicing to mine rules of Chinese medicinal physical therapies (massage, cupping and so on). The process of mining was done in two dimensions. The one-dimension analyzes the frequencies. The two-dimension analyzes the frequencies of co-existed keyword pairs. By examining the results of these two dimensions, although some noises existed, most regular knowledge of this disease is mined out. This algorithm might be useful in mining rules in the literature of traditional Chinese medicine.

Through several thousands years of clinical research and theoretical thoughts, traditional Chinese medicine (TCM) has accumulated rich experience on hypertension. However, the usage of Chinese herbal medicines (CHMs) in formulae is flexible in TCM clinical practice according to pattern differentiation. So, it is important to get the composition rules of Chinese herbal medicines through literatures. Based on the keyword list of Chinese herbal medicine, through the keyword filtering skill, we got the lists of Chinese herbal medicines. However, for Chinese herbal medicine, they are not only mentioned in the plain format of herb names, but also densely described in the form of formulae. As formulae are composed by Chinese herbal medicines according the theory of traditional Chinese medicine, so it is necessary to filtering them out and de-compose them back into specified Chinese herbal medicines. In this study, take hypertension for example, we explored the composition-rules of Chinese herbal medicines and the network of TCM pattern with them. Networks of TCM patterns and CHMs which are most frequently used in hypertension treatment are built-up and analyzed, some regularities are obtained in treating hypertension from 175011 records of literature. And this method could provide useful help for TCM clinical application and Chinese medicine research.